Summary of Mitigating Overconfidence in Out-of-distribution Detection by Capturing Extreme Activations, By Mohammad Azizmalayeri et al.
Mitigating Overconfidence in Out-of-Distribution Detection by Capturing Extreme Activations
by Mohammad Azizmalayeri, Ameen Abu-Hanna, Giovanni Cinà
First submitted to arxiv on: 21 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed solution addresses the challenge of detecting out-of-distribution (OOD) instances in machine learning models, particularly when they exhibit overconfidence. This issue is characterized by a neural network returning highly confident predictions on OOD inputs, which can lead to poor OOD detection. The approach measures extreme activation values in the penultimate layer of neural networks as a proxy for overconfidence and leverages this information to improve existing OOD detection baselines. Experimental results demonstrate substantial improvements in OOD detection AUC, with double-digit increases compared to baseline models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper solves a big problem in using machine learning models in real life. Sometimes these models get very confident about things that are actually wrong. This is called “overconfidence” and it makes it hard to know when the model’s predictions are reliable or not. The researchers came up with a new way to detect when this happens by looking at how active certain parts of the neural network are being. They tested their method on many different kinds of data, models, and scenarios, and found that it worked really well in most cases. |
Keywords
» Artificial intelligence » Auc » Machine learning » Neural network
